This book is ideal both as a first resource to discover the field of natural language processing and a guide for seasoned practitioners looking to discover the latest developments in this exciting area.
– Julian McAuley, Professor, UC San Diego
Practical NLP focuses squarely on an overlooked demographic: the practitioners and business leaders in industry!
– Zachary Lipton, Scientist at Amazon AI, Author of Dive into Deep Learning, Professor, Carnegie Mellon University
This book does a great job bridging the gap between natural language processing research and practical applications.
– Sebastian Ruder Scientist, Google DeepMind, Author of newsletter NLP News
This book offers the best of both worlds: textbooks and ‘cookbooks’. If you would like to go from zero to one in NLP, this book is for you!
– Marc Najork, Director, Google AI, ACM & IEEE Fellow
This book is a must for all aspiring NLP engineers, entrepreneurs who want to build companies around language technologies.
– Monojit Choudhury, Principal Researcher, Microsoft, Faculty at IIT Kharagpur
There is much hard-fought practical advice from the trenches. A must-read for engineers building NLP applications.
– Vinayak Hegde, CTO-in-Residence, Microsoft For Startups
I feel this is not only an essential book for NLP practitioners, it is also a valuable reference for the research community.
– Mengting Wan, Data Scientist at Airbnb, Microsoft Research Fellow
The authors achieved a rare feat by simplifying the esoteric art of design and architecture of production quality ML systems.
– Siddharth Sharma, ML Engineer, Facebook
This book gives a consolidated look at modern practice, starting from an MVP and building up to examples for sophisticated use cases.
– Ed Harris, CEO and co-founder at SharpestMinds (YC W18)
From the Inside Flap
We want to provide a holistic, yet, practical perspective which enables the reader to successfully build real world NLP solutions embedded in larger product setups. Thus, most chapters are accompanied by code walkthroughs in the associated git repository. The book is also supplemented with extensive references at the end of each chapter for the readers who want to delve deeper. Throughout the book, we start with a simple solution and incrementally build more complex solutions, by taking a Minimum Viable Product (MVP) approach, as commonly found in industry practice. We also give tips wherever possible based on our experience and learnings. Where possible, each chapter is accompanied by a discussion on the state of the art in that topic. Most chapters conclude with a case study taking real world use cases.
Consider the task of building a chatbot or text classification system at your organization. In the beginning there may be little or no data to work with. At this point a basic solution using rule based systems or traditional machine learning will be apt. As you accumulate more data, more sophisticated NLP techniques (which are often data intensive) can be used including deep learning. At each step of this journey there are dozens of alternative approaches one can take. This book will help you navigate this maze of options.
This book gives a comprehensive view on building real world NLP applications. We will cover the complete lifecycle of a typical NLP project – right from data collection to deploying and monitoring the model. Some of these steps are applicable to any ML pipeline while some are very specific to NLP. We also introduce task-specific case studies and domain-specific guides to build an NLP system from scratch. Specifically we cover a gamut tasks ranging from text classification to question answering, information extraction to dialog systems. Similarly, we provide recipes to apply these tasks in domains ranging from e-commerce to healthcare, social media to finance. Owing to the depth and breadth of the topics and scenarios we cover, we will not go step by step explaining the code and all the concepts. For details of the implementation, we have provided detailed source code notebooks. The Code snippets given in the book cover the core logic and often skip introductory steps like setting up a library or importing a package as they are covered in the associated notebooks. To cover the wide range of concepts we have given more than 450 extensive references to delve deeper into these topics. This book will be a day-to-day cookbook giving you a pragmatic view while building any NLP system as well as be a stepping stone to broaden the application of NLP into your domain.
From the Back Cover
Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor themfor particular industry verticals, this is your guide. Softwareengineers and data scientists will learn how to navigate the maze ofoptions available at each step of the journey.
About the Author
The authors have been working on NLP problems since 2006. They hail from Carnegie Mellon, UC San Diego, U of Tübingen, and the Indian Institutes of Technology. They have built and deployed NLP and ML systems in both academia and industry, including Fortune 100 companies, Silicon Valley startups, the MIT Media Lab, Microsoft Research and Google AI. They have also taught NLP courses at US universities as a faculty and published dozens of research papers in the field with hundreds of citations. The authors’ collective wisdom is distilled in the book. The book has been reviewed and advised by researchers and scientists.